Sobral, Sónia Rolland
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Data de nascimento
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Último Nome
Sobral
Primeiro Nome
Sónia Rolland
Nome
Sónia Rolland Sobral
Biografia
Licenciada em Informática de Gestão, mestre em Engenharia Eletrotécnica e de Computadores, doutora em Tecnologias e Sistemas de Informação e possui o título de agregado em Ciências da Informação. Desde 1993 é docente da Universidade Portucalense (UPT), sendo atualmente professora associada com agregação. Lecionou em diversos cursos como Engenharia Informática e Engenharia e Gestão Industrial, em diversas instituições como Lodz University of Technology e a Universidade de Aveiro, e em diversos países como Angola e Cabo Verde. Participou em diferentes órgãos, tendo sido presidente do Conselho Pedagógico da UPT. Pertence à comissão de várias conferências internacionais e revistas científicas. É autora de uma centena de publicações, a sua maioria indexadas na SCOPUS e/ou WoS. É membro integrado no REMIT – Research on Economics, Management and Information Technologies, sendo atualmente coordenadora de um dos dois grupos de investigação (Transformação Digital e Inovação nas Organizações).
Afiliação:
REMIT – Research on Economics, Management and Information Technologies.
DCT - Departamento de Ciência e Tecnologia.
Identificadores
ED15-C9EC-5996
37091626900
G-2227-2014Projetos de investigação
Unidades organizacionais
REMIT – Research on Economics, Management and Information Technologies
Centro de investigação que que tem como objetivo principal produzir e disseminar conhecimento teórico e aplicado que possibilite uma maior compreensão das dinâmicas e tendências económicas, empresariais, territoriais e tecnológicas do mundo contemporâneo e dos seus efeitos socioeconómicos. O REMIT adota uma perspetiva multidisciplinar que integra vários domínios científicos: Economia e Gestão; Ciências e Tecnologia; Turismo, Património e Cultura.
Founded in 2017, REMIT – Research on Economics, Management and Information Technologies is a research unit of Portucalense University. Based on a multidisciplinary and interdisciplinary perspective it aims at responding to social challenges through a holistic approach involving a wide range of scientific fields such as Economics, Management, Science, Technology, Tourism, Heritage and Culture.
Grounded on the production of advanced scientific knowledge, REMIT has a special focus on its application to the resolution of real issues and challenges, having as strategic orientations:
- the understanding of local, national and international environment;
- the development of activities oriented to professional practice, namely in the business world.
5 resultados
Resultados da pesquisa
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Publicação Acesso Aberto How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review2021-11-04 - Oliveira, Catarina Félix de; Sobral, Sónia Rolland; Ferreira, Maria João; Moreira, FernandoRetention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.Publicação Acesso Aberto Predicting students' performance using survey data2020 - Oliveira, Catarina Félix de; Sobral, Sónia RollandThe acquisition of competences for the development of computer programs is one of the main challenges faced by computer science students. As a result of not being able to develop the abilities needed (for example, abstraction), students drop out the subjects and sometimes even the course. There is a need to study the causes of student success (or failure) in introductory curricular units to check for behaviours or characteristics that may be determinant and thus try to prevent and change said causes. The students of one programming curricular unit were invited to answer four surveys. We use machine learning techniques to try to predict the students’ grades based on the answers obtained on the surveys. The results obtained enable us to plan the semester accordingly, by anticipating how many students might need extra support. We hope to increase the students’ motivation and, with this, increase their interest on the subject. This way we aim to accomplish our ultimate goal: reducing the drop out and increasing the overall average student performance.Publicação Acesso Aberto Predicting students performance in introductory programming courses: a literature review2021 - Oliveira, Catarina Félix de; Sobral, Sónia RollandThe teaching-learning process in programming in university with freshmen is often associated with high failure and dropout rates. These outcomes frustrate both students and teachers and there is a need to verify the causes of these failures. By predicting the causes of these problems, we can try to control them, or at least try to plan the courses to try to avoid failure in the identified cases. The purpose of this paper is to analyze the scientific production concerning the prediction of students’ performance in introductory programming courses. This analysis regards articles indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. The sample includes a total of 30 articles. The results obtained by bibliometric analysis show when and where those documents were published, who are the authors and what is the focus of said articles. We also analyzed the most cited documents. We made a summary of the articles. We were able to obtain a global overview of the theme, obtaining a strong analysis that is useful for teachers in the process of helping students achieve success in introductory programming courses at universities.Publicação Acesso Aberto Interpretable success prediction in a computer networks curricular unit using machine learning2024-07-25 - Oliveira, Catarina Félix de; Sobral, Sónia Rolland; Ferreira, Maria João; Moreira, FernandoToday, higher education institutions are focused on understanding which factors are associated with the failure or success of students to, early on, be able to implement measures that can reduce the low performance of students and even dropout. The retention rate is positively and negatively influenced by factors belonging to several dimensions (personal, environmental, and institutional). We aim to use information from those dimensions to identify students enrolled in a Computer Networks course at risk of failing the subject. Besides, this needs to happen as early as possible, to be able to provide the students, for example, with extra support or resources to try to prevent that negative outcome. For predicting the grade level on the first test, the best accuracy obtained was 55%. However, most C-level grades were correctly classified, with 63% accuracy in predicting the students that are most at risk of failing, which is one of our main objectives. As for the prediction of the second test’s grade level, the best accuracy obtained was 89% and concerned data regarding the students’ interaction with the LMS together with students’ grades history. All the C-level grades were correctly classified (100% accuracy) and so we were able to correctly predict every student at a high risk of failing. Using the procedure described in this paper, we are able to anticipate the students needing extra support, and provide them with different resources, to try to prevent their negative outcome.Publicação Acesso Aberto Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study2021-12-18 - Oliveira, Catarina Félix de; Sobral, Sónia RollandSelf-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment